Estimation of cross-sectional areas of individual tree trunks using remotely collected
Name: GABRIEL LESSA DA SILVA LAVAGNOLI
Publication date: 09/04/2025
Examining board:
Name |
Role |
|---|---|
| ADRIANO RIBEIRO DE MENDONCA | Examinador Interno |
| ANDRÉ QUINTÃO DE ALMEIDA | Examinador Externo |
| CARLOS PEDRO BOECHAT SOARES | Examinador Externo |
| DIOGO NEPOMUCENO COSENZA | Examinador Externo |
| GILSON FERNANDES DA SILVA | Presidente |
Summary: This thesis investigates the accuracy in the measurement of tree trunk cross-sectional areas, highlighting the practical importance of this variable for forest inventories and its implications for volume and biomass estimates. The work is structured into two complementary studies. The first study evaluates the impacts of convexity and isoperimetric deficits on traditional measurement methods, such as calipers and diameter tapes, comparing them to a photographic method developed by the author, which calculates areas and estimates contours through pixel counting. The results showed that traditional methods exhibit significant systematic errors, arising from the incorrect assumption of perfect circularity of cross-sections, whereas the photographic method demonstrated high precision, with mean relative errors below 0.1%. The second study proposes a computational methodology for estimating cross-sectional areas from point clouds obtained using a GeoSLAM LiDAR sensor, comparing the measurements with those obtained from a high-precision infrared scanner (EinScan). The research involved the analysis of 56 eucalyptus trees, comprising more than 1,000 cross-sections. Additional simulations of traditional methods were also conducted for direct comparison. It was observed that traditional techniques, once again, tended to overestimate the areas (with a mean bias of approximately 2.8%), while the LiDAR-based method showed the opposite trend, with a mean bias of -8.12%. However, after applying a specific mathematical correction, the LiDAR estimates achieved excellent accuracy, with a relative root mean square error (RMSE) of 2.4%, a mean relative bias close to zero, and a mean absolute relative error (MAE) of 1.65%, demonstrating great potential for practical applications after appropriate adjustments.
Key-words: seccional area, convexity deficit, isoperimetric deficit, forest inventory, LiDAR
